A new sea ice state dependent parameterization for the free drift of sea ice

Abstract

Abstract. Free drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motion vectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables (concentration and thickness) and ocean current (as a residual). Building on the fact that the spatially varying standard wind-ice transfer coefficient (considering only surface wind stress) has a structure as the spatial distribution of sea ice thickness, we introduce a wind-ice transfer coefficient that scales linearly with thickness. Results show a mean error of −0.5 cm/s (low-speed bias) and a root-mean-square error of 5.1 cm/s, considering daily buoy drift data as truth. This represents a 31 % reduction of the error on drift speed compared to the free drift estimates used in the Polar Pathfinder dataset. The thickness-dependent wind transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum) drift speed in May (October), compared to July (January) for the constant wind transfer coefficient parameterizations which simply follow the peak in mean surface wind stresses. The trend in sea ice drift in this new model is +0.45 cm/s decade−1 compared with +0.39 cm/s decade−1 from the buoy observations, whereas there is essentially no trend in the standard free drift parameterization (−0.01 cm/s decade−1) or the Polar Pathfinder free drifts (−0.03 cm/s decade−1). The wind turning angle that minimize the cost function is equal of 25°, with a mean and root-mean square error of +2.6° and 51° on the direction of the drift, respectively. The residual from the minimization procedure (i.e. the ocean currents) resolves key large scale features such as the Beaufort Gyre and Transpolar Drift Stream, and is in good agreement with ocean state estimates from the ECCO, GLORYS and PIOMAS ice-ocean reanalyses, and geostrophic currents from dynamical ocean topography, with a root-mean-square difference of 2.4, 2.9, 2.6 and 3.8 cm/s, respectively. Finally, a repeat of the analysis on a two sub-section of the time series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion of the gyre in the post-2000 concurrent with a thinning of the sea ice cover and observations acceleration of the ice drift speed and ocean current. This new dataset is publicly available for complementing merged observations-based sea ice drift datasets that includes satellite and buoy drift records.

Unsupervised clustering of oceanic Lagrangian particles: identification of the main pathways of the Labrador Current

“…Argo floats, surface drifters, RAFOS floats) and balloons in the atmosphere, as well as from the advection of virtual particles derived from velocity fields reconstructed from satellite altimetry or output from numerical simulations (among others, A. F. Thompson & Sallée, 2012;van Sebille et al, 2018). Lagrangian trajectories are used to study ocean and atmospheric circulations (e.g., Schulze Chretien & Frajka- Williams, 2018;Gillard et al, 2016;Bower et al, 2011;Fischer & Schott, 2002) and sea ice drift (e.g., Williams et al, 2016;Brunette et al, 2019), to identify the origin and fate of water masses (e.g., Kawasaki et al, 2022;Kelly et al, 2019), to assess connectivity timescales (e.g., Jönsson & Watson, 2016), and to study the fate of atmospheric and oceanic pollutants (e.g., Hertwig et al, 2015;Viikmäe et al, 2013), plastic (e.g., Lebreton et al, 2012), larvae (e.g., Ayata et al, 2010;Cetina-Heredia et al, 2015;Phelps et al, 2015;Simons et al, 2013), icebergs (e.g., Marson et al, 2018;Merino et al, 2016), and debris or people during search and rescue (e.g., Hart-Davis & Backeberg, 2021). Yet, sets of Lagrangian trajectories are challenging to analyze.…”

Bridging Gaps in the Climate Observation Network: A Physics‐Based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data‐Driven Stochastic Models

“…On the other hand, in situ field measurements using buoys on ice floe surfaces have provided invaluable information. However, trajectories are often sparse (Brunette et al., 2022; Gabrielski et al., 2015; Hutchings et al., 2012; Itkin et al., 2017; Lei et al., 2020), with coastal regions undersampled relative to the central Arctic.…”